Evaluation of Turbulence Models for Thermal Striping in a Triple Jet

2006 ◽  
Vol 129 (4) ◽  
pp. 583-592 ◽  
Author(s):  
Seok-Ki Choi ◽  
Seong-O Kim
Keyword(s):  
Experimental Data ◽  
Root Mean Square ◽  
Transport Model ◽  
Computational Study ◽  
Turbulence Models ◽  
Oscillatory Behavior ◽  
Mean Square ◽  
Relaxation Model ◽  
Elliptic Relaxation ◽  
Thermal Striping

A computational study for an evaluation of the current turbulence models for the prediction of a thermal striping in a triple jet is performed. The tested turbulence models are the two-layer model, the shear stress transport model, and the elliptic relaxation model. These three turbulence models are applied to the prediction of a thermal striping in a triple jet in which detailed experimental data are available. The predicted time-averaged and root-mean-square values of the temperature are compared with the experimental data, and the capability of predicting the oscillatory behavior of the ensemble-averaged temperature is investigated. From these works, it is shown that only the elliptic relaxation model is capable of predicting the oscillatory behavior of the ensemble-averaged temperature. It is also shown that the elliptic relaxation model predicts best the time-averaged temperature and the root mean square of the temperature fluctuation. However, this model predicts a slower mixing at the far downstream of the jet.

Evaluation of Turbulence Models for Thermal Striping in a Triple-Jet

2005 ◽  
Author(s):  
Seok-Ki Choi ◽  
Ho-Yun Nam ◽  
Myung-Hwan Wi ◽  
Seong-O Kim ◽  
Jong-Chull Jo ◽  
...  
Keyword(s):  
Experimental Data ◽  
Root Mean Square ◽  
Transport Model ◽  
Computational Study ◽  
Turbulence Models ◽  
Oscillatory Behavior ◽  
Mean Square ◽  
Relaxation Model ◽  
Elliptic Relaxation ◽  
Thermal Striping

A computational study for the evaluation of the current turbulence models for the prediction of a thermal striping in a triple-jet is performed. The tested turbulence models are the two-layer model, the shear stress transport model and the elliptic relaxation model. These three turbulence models are applied to the prediction of the thermal striping in a triple-jet in which detailed experimental data are available. The performances of the tested turbulence models are evaluated through comparisons with the experimental data. The predicted mean and root-mean-square values of the temperature are compared with the experimental data, and the capability of predicting the oscillatory behavior of the ensemble-averaged temperature is investigated. From these works it is shown that only the elliptic relaxation model is capable of predicting the oscillatory behavior of the ensemble-averaged temperature. It is also shown that the elliptic relaxation model predicts best the time-averaged and root-mean-square of the temperature fluctuation. However, this model predicts a slower mixing at the far downstream of the jet.

The Role of Turbulence Models for Predicting a Thermal Stratification

2006 ◽  
Vol 128 (4) ◽  
pp. 656-662 ◽  
Author(s):  
Seok-Ki Choi ◽  
Seong-O Kim
Keyword(s):  
Liquid Metal ◽  
Thermal Stratification ◽  
Transport Model ◽  
Numerical Study ◽  
Turbulence Models ◽  
Liquid Metal Reactor ◽  
Temporal Oscillation ◽  
Elliptic Relaxation ◽  
Shear Stress Transport Model

A numerical study of the evaluation of turbulence models for predicting the thermal stratification phenomenon is presented. The tested models are the elliptic blending turbulence model (EBM), the two-layer model, the shear stress transport model (SST), and the elliptic relaxation model (V2-f). These four turbulence models are applied to the prediction of a thermal stratification in an upper plenum of a liquid metal reactor experimented at the Japan Nuclear Cooperation (JNC). The EBM and V2-f models predict properly the steep gradient of the temperature at the interface of the cold and hot regions that is observed in the experimental data, and the EBM and V2-f models have the capability of predicting the temporal oscillation of the temperature. The two-layer and SST models predict the diffusive temperature gradient at the interface of a thermal stratification and fail to predict a temporal oscillation of the temperature. In general, the EBM predicts best the thermal stratification phenomenon in the upper plenum of the liquid metal reactor.

CFD Studies on a Large Diameter Jet Impingement Flow

2004 ◽  
Author(s):  
Zhiguo Zhang ◽  
Mounir Ibrahim
Keyword(s):  
Experimental Data ◽  
Computational Study ◽  
Large Diameter ◽  
Turbulence Models ◽  
Jet Impingement ◽  
Reynolds Numbers ◽  
University Of Minnesota ◽  
Inlet Conditions ◽  
The University ◽  
The Impact

This paper presents computational study for a large diameter (216 mm) and small space ratios (S/D = 0.25 and 0.5) jet impingement flow. CFD-ACE code was used as the computational tools; the code was first validated by comparing its predictions with both CFD and experimental data from the literature. Then, the study was performed for two different Reynolds numbers: 7600, 17700 and two different space ratios: 0.25 and 0.5. Also two different turbulence models were utilized in this study: low Reynolds number turbulent k-ε and k-ω. The CFD results were compared with flow visualization results conducted at the University of Minnesota for the same configurations. The impact of choosing different inlet conditions on the CFD flow field was examined. The k-ε model showed greater sensitivity to the selection of the inlet conditions. Moreover, the k-ω model showed much better agreement with the experimental data than the k-ε model.

Computational Modelling of Tip Heat Transfer to a Super-Scale Model of an Unshrouded Gas Turbine Blade

2008 ◽  
Author(s):  
Brian M. T. Tang ◽  
Pepe Palafox ◽  
David R. H. Gillespie ◽  
Martin L. G. Oldfield ◽  
Brian C. Y. Cheong
Keyword(s):  
Heat Transfer ◽  
Experimental Data ◽  
Nusselt Number ◽  
Gas Turbine ◽  
Turbine Blade ◽  
Computational Study ◽  
Turbulence Models ◽  
Scale Model ◽  
Tip Leakage Flow ◽  
Blade Tip

Control of over-tip leakage flow between turbine blade tips and the stationary shroud is one of the major challenges facing gas turbine designers today. The flow imposes large thermal loads on unshrouded high pressure turbine blades and is significantly detrimental to turbine blade life. This paper presents results from a computational study performed to investigate the detailed blade tip heat transfer on a sharp-edged, flat tip HP turbine blade. The tip gap is engine representative at 1.5% of the blade chord. Nusselt number distributions on the blade tip surface have been obtained from steady flow simulations and are compared to experimental data carried out in a super-scale cascade, which allows detailed flow and heat transfer measurements in stationary and engine representative conditions. Fully structured, multiblock hexahedral meshes were used in the simulations, performed in the commercial solver Fluent. Seven industry-standard turbulence models, and a number of different tip gridding strategies are compared, varying in complexity from the one-equation Spalart-Allmaras model to a seven-equation Reynolds Stress model. Of the turbulence models examined, the standard k-ω model gave the closest agreement to the experimental data. The discrepancy in Nusselt number observed was just 5%. However, the size of the separation on the pressure side rim was underpredicted, causing the position of reattachment to occur too close to the edge. Other turbulence models tested typically underpredicted Nusselt numbers by around 35%, although locating the position of peak heat flux correctly. The effect of the blade to casing motion was also simulated successfully, qualitatively producing the same changes in secondary flow features as were previously observed experimentally, with associated changes in heat transfer to the blade tip.

scholarly journals Improving the prediction of an atmospheric chemistry transport model using gradient-boosted regression trees

2020 ◽  
Vol 20 (13) ◽  
pp. 8063-8082 ◽  
Author(s):  
Peter D. Ivatt ◽  
Mathew J. Evans
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Atmospheric Chemistry ◽  
Transport Model ◽  
Mean Square ◽  
Model Bias ◽  
Chemistry Transport Model ◽  
Pearson's R ◽  
Process Based Models

Abstract. Predictions from process-based models of environmental systems are biased, due to uncertainties in their inputs and parameterizations, reducing their utility. We develop a predictor for the bias in tropospheric ozone (O3, a key pollutant) calculated by an atmospheric chemistry transport model (GEOS-Chem), based on outputs from the model and observations of ozone from both the surface (EPA, EMEP, and GAW) and the ozone-sonde networks. We train a gradient-boosted decision tree algorithm (XGBoost) to predict model bias (model divided by observation), with model and observational data for 2010–2015, and then we test the approach using the years 2016–2017. We show that the bias-corrected model performs considerably better than the uncorrected model. The root-mean-square error is reduced from 16.2 to 7.5 ppb, the normalized mean bias is reduced from 0.28 to −0.04, and Pearson's R is increased from 0.48 to 0.84. Comparisons with observations from the NASA ATom flights (which were not included in the training) also show improvements but to a smaller extent, reducing the root-mean-square error (RMSE) from 12.1 to 10.5 ppb, reducing the normalized mean bias (NMB) from 0.08 to 0.06, and increasing Pearson's R from 0.76 to 0.79. We attribute the smaller improvements to the lack of routine observational constraints for much of the remote troposphere. We show that the method is robust to variations in the volume of training data, with approximately a year of data needed to produce useful performance. Data denial experiments (removing observational sites from the algorithm training) show that information from one location (for example Europe) can reduce the model bias over other locations (for example North America) which might provide insights into the processes controlling the model bias. We explore the choice of predictor (bias prediction versus direct prediction) and conclude both may have utility. We conclude that combining machine learning approaches with process-based models may provide a useful tool for improving these models.

Computational Modeling of Tip Heat Transfer to a Superscale Model of an Unshrouded Gas Turbine Blade

2010 ◽  
Vol 132 (3) ◽  
Author(s):  
Brian M. T. Tang ◽  
Pepe Palafox ◽  
Brian C. Y. Cheong ◽  
Martin L. G. Oldfield ◽  
David R. H. Gillespie
Keyword(s):  
Heat Transfer ◽  
Experimental Data ◽  
Nusselt Number ◽  
Gas Turbine ◽  
Turbine Blade ◽  
Computational Study ◽  
Turbine Blades ◽  
Turbulence Models ◽  
Tip Leakage Flow ◽  
Blade Tip

Control of over-tip leakage flow between turbine blade tips and the stationary shroud is one of the major challenges facing gas turbine designers today. The flow imposes large thermal loads on unshrouded high pressure (HP) turbine blades and is significantly detrimental to turbine blade life. This paper presents results from a computational study performed to investigate the detailed blade tip heat transfer on a sharp-edged, flat tip HP turbine blade. The tip gap is engine representative at 1.5% of the blade chord. Nusselt number distributions on the blade tip surface have been obtained from steady flow simulations and are compared with experimental data carried out in a superscale cascade, which allows detailed flow and heat transfer measurements in stationary and engine representative conditions. Fully structured, multiblock hexahedral meshes were used in the simulations performed in the commercial solver FLUENT. Seven industry-standard turbulence models and a number of different tip gridding strategies are compared, varying in complexity from the one-equation Spalart–Allmaras model to a seven-equation Reynolds stress model. Of the turbulence models examined, the standard k-ω model gave the closest agreement to the experimental data. The discrepancy in Nusselt number observed was just 5%. However, the size of the separation on the pressure side rim was underpredicted, causing the position of reattachment to occur too close to the edge. Other turbulence models tested typically underpredicted Nusselt numbers by around 35%, although locating the position of peak heat flux correctly. The effect of the blade to casing motion was also simulated successfully, qualitatively producing the same changes in secondary flow features as were previously observed experimentally, with associated changes in heat transfer with the blade tip.

Numerical Simulation of Separation Control in Backward Facing Step Turbulent Flow

2002 ◽  
Author(s):  
Emmanuel Guilmineau ◽  
Patrick Queutey
Keyword(s):  
Experimental Data ◽  
Reynolds Stress ◽  
Transport Model ◽  
Turbulence Models ◽  
Separated Flow ◽  
Separation Control ◽  
Local Perturbation ◽  
Backward Facing Step ◽  
Reynolds Stress Transport Model ◽  
Turbulent Separated Flow

The control of turbulent separated flow over the backward-facing step is numerically investigated with various turbulence models ranging from one equation Spalart & Allmaras (1992), two-equation K-ω closures (Wilcox, 1988; Menter, 1993) to a full Reynolds stress transport model based on the Reynolds stress transport Rij-ω model (Deng & Visonneau, 1999). Results are compared with experimental data of Yoshioka et al. (1999) where the flow control was monitoring with alternating suction/injection at the step height. It is shown that the effect of that local perturbation is better represented using the Rij-ω turbulence model.

scholarly journals Computational study on Strontium ion modified Fibronectin-Hydroxyapatite interaction

2021 ◽  
Author(s):  
Subhadip Basu ◽  
Bikramjit Basu ◽  
Prabal Kumar Maiti
Keyword(s):  
Molecular Dynamics ◽  
Surface Modification ◽  
Root Mean Square ◽  
Computational Study ◽  
Biological Response ◽  
Md Simulations ◽  
Dynamics Simulation ◽  
Cell Surface Receptor ◽  
Isomorphic Substitution ◽  
Mean Square

Protein adsorption is the first key step in cell-material interaction. The initial phase of such adsorption process can only be probed using modelling approaches like molecular dynamics (MD) simulation. Despite a large number of studies on the adsorption behaviour of proteins on different biomaterials including hydroxyapatite (HA); little attention has been paid towards quantitative assessment of the effects of various physicochemical influencers like surface modification, pH, and ionic strength. Among these factors, surface modification through isomorphic substitution of foreign ions inside the apatite structure is of particular interest in the context of protein-HA interaction as it is widely used to tailor the biological response of HA. Given this background, we present here the molecular-level understanding of fibronectin (FN) adsorption mechanism and kinetics on Sr2+-doped HA (001) surface, at 300K by means of all atom molecular dynamics simulation. Electrostatic interaction involved in adsorption of FN on HA was found to be significantly modified in presence of Sr2+ doping in apatite lattice. In harmony with the published experimental observations, the Sr-doped surface was found to better support FN adhesion compared to pure HA, with 10 mol% Sr-doped HA exhibiting best FN adsorption. Sr2+ ions also influence the stability of the secondary structure of FN, as observed from the root mean square deviation (RMSD) and root mean square fluctuation (RMSF) analysis. The presence of Sr2+ enhances the flexibility of specific residues (residue no. 20-44, 74-88) of the FN module. Rupture forces to disentangle FN from the biomaterials surface, obtained from steered molecular dynamics (SMD) simulations, were found to corroborate well with the results of equilibrium MD simulations. One particular observation is that, the availability of RGD motif for the interaction with cell surface receptor integrin is not significantly influenced by Sr2+ substitution. Summarizing, the present work establishes a quantitative foundation towards the molecular basis of the earlier experimentally validated better cytocompatibility of Sr-doped HA.

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